The normalized difference vegetation index of small Douglas-fir canopies with varying chlorophyll concentrations

Yoder, Barbara J.; Waring, Richard H.

doi:10.1016/0034-4257(94)90061-2

Cited by 157 publications

(86 citation statements)

References 33 publications

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“…Apparently, the effect of treatments on 557 nm is minimum, whereas 603 nm is highly effected by treatments. It is also worth noting that 603 nm is separate from the strong chlorophyll absorption band 680 nm, which is less sensitive to more subtle changes in photosynthesis pigment concentrations [98,99]. A very similar and significant combination of spectral regions (R 2 is highest at 704 and 540 nm) was found for Fs and Fm', and these results somewhat agree with the findings of Stratoulias et al [40], who reported the combination of similar spectral regions (including red and far-red fluorescence emission regions) correlated with Fs and Fm' for deep water reed plants with lower chlorophyll concentration, but the shapes of the R 2 maps from their study showed broad significant spectral regions with diffuse edges compared to the ones in our study (Figure 4b,c).…”

Section: Discussionmentioning

confidence: 99%

Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy

et al. 2017

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Early detection of water stress is critical for precision farming for improving crop productivity and fruit quality. To investigate varying rootstock and irrigation interactions in an open agricultural ecosystem, different irrigation treatments were implemented in a vineyard experimental site either: (i) nonirrigated (NIR); (ii) with full replacement of evapotranspiration (FIR); or (iii) intermediate irrigation (INT, 50% replacement of evapotranspiration). In the summers 2014 and 2015, we collected leaf reflectance factor spectra of the vineyard using field spectroscopy along with grapevine physiological parameters. To comprehensively analyze the field-collected hyperspectral data, various band combinations were used to calculate the normalized difference spectral index (NDSI) along with 26 various indices from the literature. Then, the relationship between the indices and plant physiological parameters were examined and the strongest relationships were determined. We found that newly-identified NDSIs always performed better than the indices from the literature, and stomatal conductance (G s ) was the plant physiological parameter that showed the highest correlation with NDSI(R 603 ,R 558 ) calculated using leaf reflectance factor spectra (R 2 = 0.720). Additionally, the best NDSI(R 685 ,R 415 ) for non-photochemical quenching (NPQ) was determined (R 2 = 0.681). G s resulted in being a proxy of water stress. Therefore, the partial least squares regression (PLSR) method was utilized to develop a predictive model for G s . Our results showed that the PLSR model was inferior to the NDSI in G s estimation (R 2 = 0.680). The variable importance in the projection (VIP) was then employed to investigate the most important wavelengths that were most effective in determining G s . The VIP analysis confirmed that the yellow band improves the prediction ability of hyperspectral reflectance factor data in G s estimation. The findings of this study demonstrate the potential of hyperspectral spectroscopy data in motoring plant stress response.

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Section: Discussionmentioning

confidence: 99%

Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy

et al. 2017

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“…The selection of the NIR band was not surprising: multiple scattering inside leaves and between leaves in the canopy is the governing factor of reflectance in this spectral region, as documented in pioneering studies (e.g., [48]). The green range of the spectrum was found to be more sensitive to moderate to high LAI due to the much lower absorption coefficient of chlorophyll in the green than in the red region which prevents saturation of reflectance and provides deeper light penetration inside the leaves and canopy (e.g., [49][50][51][52][53]). The increase in accuracy when a third, blue, band was added may be explained by the high and similar (in both crops) sensitivity of blue reflectance to green LAI below 3 [21].…”

Section: Optimal Spectral Samplingmentioning

confidence: 99%

Toward Generic Models for Green LAI Estimation in Maize and Soybean: Satellite Observations

et al. 2017

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Abstract:Informative spectral bands for green leaf area index (LAI) estimation in two crops were identified and generic models for soybean and maize were developed and validated using spectral data taken at close range. The objective of this paper was to test developed models using Aqua and Terra MODIS, Landsat TM and ETM+, ENVISAT MERIS surface reflectance products, and simulated data of the recently-launched Sentinel 2 MSI and Sentinel 3 OLCI. Special emphasis was placed on testing generic models which require no re-parameterization for these species. Four techniques were investigated: support vector machines (SVM), neural network (NN), multiple linear regression (MLR), and vegetation indices (VI). For each technique two types of models were tested based on (a) reflectance data, taken at close range and resampled to simulate spectral bands of satellite sensors; and (b) surface reflectance satellite products. Both types of models were validated using MODIS, TM/ETM+, and MERIS data. MERIS was used as a prototype of OLCI Sentinel-3 data which allowed for assessment of the anticipated accuracy of OLCI. All models tested provided a robust and consistent selection of spectral bands related to green LAI in crops representing a wide range of biochemical and structural traits. The MERIS observations had the lowest errors (around 11%) compared to the remaining satellites with observational data. Sentinel 2 MSI and OLCI Sentinel 3 estimates, based on simulated data, had errors below 8%. However the accuracy of these models with actual MSI and OLCI surface reflectance products remains to be determined.

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“…To overcome this difficulty, numerous studies have used high-frequency coverage of the terrestrial biosphere by the National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) to quantify ecosystem vegetation phenology. The normalized difference vegetation index (NDVI), calculated as (N-R) / (N+R), where N is the nearinfrared reflectance and R is the red reflectance, has been related to several biophysical parameters including chlorophyll density , percent canopy cover [Yoder and Waring, 1994], absorbed photosynthetically active radiation [Myneni and Williams, 1994], leaf area index [Spanner et at., 1990b], and productivity [Prince et at., 1995]. Potentially, NDVI ranges from -1 to 1, but Earth surfaces are usually limited to -0.1-0.7 NDVI.…”

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confidence: 99%

A continental phenology model for monitoring vegetation responses to interannual climatic variability

White¹,

Thornton²,

Running³

1997

Global Biogeochemical Cycles

1,078

883

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Abstract. Regional phenology is important in ecosystem simulation models and coupled biosphere/atmosphere models. In the continental United States, the timing of the onset of greenness in the spring (leaf expansion, grass green-up) and offset of greenness in the fall (leaf abscission, cessation of height growth, grass brown-off) are strongly influenced by meteorological and climatological conditions. We developed predictive phenology models based on traditional phenology research using commonly available meteorological and climatological data. Predictions were compared with satellite phenology observations at numerous 20 km x 20 km contiguous landcover sites. Onset mean absolute error was 7.2 days in the deciduous broadleaf forest (DBF) biome and 6.1 days in the grassland biome. Offset mean absolute error was 5.3 days in the DBF biorne and 6.3 days in the grassland biome. Maximum expected errors at a 95% probability level ranged from 10 to 14 days. Onset was strongly associated with temperature summations in both grassland and DBF biomes; DBF offset was best predicted with a photoperiod function, while grassland offset required a combination of precipitation and temperature controls.

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The normalized difference vegetation index of small Douglas-fir canopies with varying chlorophyll concentrations

Cited by 157 publications

References 33 publications

Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy

Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy

Toward Generic Models for Green LAI Estimation in Maize and Soybean: Satellite Observations

A continental phenology model for monitoring vegetation responses to interannual climatic variability

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